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Denoising Brain Images with the Aid of Discrete Wavelet Transform and Monarch Butterfly Optimization with Different Noises

Journal of Medical Systems volume 42, Article number: 207 (2018) Cite this article

Abstract

The aim of this paper is to denoise the high noise density image proficiently with minimal computation cost using various techniques. This paper proposes an approach for image denoising based on Discrete Wavelet Transform (DWT) in association with Monarch Butterfly Optimization (MBO) technique. Along with Gaussian noise, salt & pepper and speckle noise are added to the image and DWT is applied on the noisy image. The Haar wavelet is used to segregate the sub bands and threshold operation is carried out in three bands. The wavelet coefficient optimization process is performed for optimizing the coefficient value with the assistance of MBO technique. In this wavelet-optimized parameter, the inverse DWT (IDWT) is applied. The proposed method decreases the noise from images more efficiently. The result exhibits that MBO technique is better than the existing and other traditional techniques as it minimize the mean square error (MSE). This process is validated with PSNR value and implemented in MATLAB.

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Affiliations

  1. Sathyabama University, Chennai, India

    T. E. Aravindan

  2. Department of ECE, Anna University, Chennai, India

    R. Seshasayanan

Authors
  1. T. E. Aravindan
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  2. R. Seshasayanan
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Correspondence to T. E. Aravindan.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Image & Signal Processing

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Aravindan, T.E., Seshasayanan, R. Denoising Brain Images with the Aid of Discrete Wavelet Transform and Monarch Butterfly Optimization with Different Noises. J Med Syst 42, 207 (2018). https://doi.org/10.1007/s10916-018-1069-4

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  • DOI: https://doi.org/10.1007/s10916-018-1069-4

Keywords

  • Brain images
  • Discrete wavelet transform (DWT)
  • Monarch butterfly optimization (MBO)
  • Peak signal to noise ratio (PSNR) and mean square error (MSE)